Next-Gen Computing: Exploring Industry 4.0
Keywords:
Industry 4.0, Smart Technologies, IoTAbstract
Industry 4.0 represents a paradigm shift in manufacturing and beyond, driven by advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and cyber-physical systems (CPS). This abstract explores the transformative impact of Industry 4.0 on computing, focusing on its integration into traditional manufacturing processes and broader applications in various sectors. Next-generation computing in Industry 4.0 emphasizes interconnectedness, automation, and real-time data analytics to optimize production efficiency, reduce costs, and enhance product quality. Key technologies like cloud computing, edge computing, and big data analytics play pivotal roles in enabling smart factories and decentralized decision-making capabilities. Moreover, Industry 4.0 fosters a shift towards autonomous systems and adaptive manufacturing environments, where machines communicate and collaborate independently, guided by real-time data insights. This abstract discusses the implications of these advancements for computer science, highlighting opportunities and challenges in cyber security, data privacy, and workforce reskilling. Ultimately, the exploration of Industry 4.0 in next-gen computing underscores its potential to revolutionize industries, reshape business models, and pave the way for a new era of interconnected and intelligent systems.
References
[1] Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Final report of the Industrie 4.0 Working Group.
[2] Lee, J., Bagheri, B., & Kao, H. (2015). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23. https://doi.org/10.1016/j.mfglet.2014.12.01
[3] Lu, Y., Xu, X., & Xu, X. (2017). The internet of things: From RFID to the next-generation pervasive networked systems. Journal of Ambient Intelligence and Humanized Computing, 8(5), 735-749. https://doi.org/10.1007/s12652-017-0492-5
[4] Tao, F., Cheng, Y., Xu, L. D., Zhang, L., Li, B. H., & Hu, S. J. (2018). Advanced manufacturing systems: Socialization, decentralization and intelligence. International Journal of Advanced Manufacturing Technology, 94(9-12), 3563-3576. https://doi.org/10.1007/00170-017-0569-5
[5] Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in Industry 4.0. Procedia CIRP, 40, 536-541. https://doi.org/10.1016/j.procir.2016.01.19
[6] Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruptions in supply chains caused by COVID-19. International Journal of Production Research, 58(1), 290-305. https://doi.org/10.1080/00207543.2020.1796327
[7] Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI. Harvard Business Review Press. ISBN: 9781633697629
[8] Schuh, G., Anderl, R., & Gausemeier, J. (2017). Industrie 4.0 maturity index—Managing the digital transformation of companies. Acatech STUDY
[9] Yin, S., Kaynak, O., & Bayrak, C. (2018). Big data for cyber-physical systems in industry 4.0: A survey. Journal of Industrial Information Integration, 10, 1-9. https://doi.org/10.1016/j.jii.2018.01.004
[10] Mourtzis, D., & Vlachou, E. (2018). Virtual and augmented reality applications in manufacturing. Procedia CIRP, 78, 327-332.https://doi.org/10.1016/j.procir.2018.08.248
[11] Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard Business Review, 93(10), 96-114.
[12] Cascio, W. F., & Montealegre, R. (2016). How technology is changing work and organizations. Annual Reviewof Organizational Psychology and Organizational Behavior, 3(1), 349-375. https://doi.org/10.1146/annurevorgpsych-041015-062352.
[13] Banerjee, B., & Patel, J. T. (2016). A symmetric key block cipher to provide confidentiality in wireless sensor networks. Infocomp journal of computer science, 15(1), 12-18.
[14] Banerjee, B. (2019). Avalanche effect: A judgement parameter of strength in symmetric key block ciphers. International journal of engineering development and research, 7(2), 116-121.
[15] Banerjee, B., Jani, A., Shah, N., & Patel, A. (2020). Post Quantum Security Enhancement Scheme in IoT Blockchain Framework. GIS Science Journal, 7(6), 664-672.
[16] Patel, M. K., Uchhula, V. V., & Banerjee, B. (2013). Comparative Analysis of Routing Protocols in MANET Based on Packet Delivery Ratio using NS2. Int. J. Adv. Res. Comput. Sci. Softw. Eng, 3(11), 172-177.
[17] Banerjee, B., Jani, A., & Shah, N. (2021). Asymmetric confidentiality in blockchain embedded smart grids in galois field. Frontiers in Blockchain, 4, 770074.
[18] Banerjee, B., Jani, A., & Shah, N. (2021). Traditional and quantum approaches against shor’s algorithm: A review. International journal of research publication and reviews, 2(2), 6.
[19] Mehta, J., Panwar, D. S., Ghardesia, S., Chauhan, A., Atodariya, V. V., Banerjee, B., ... & Bhakhar, M. S. (2020). Drying of banana-stepwise effect in drying air temperature on drying kinetics. The Open Chemical Engineering Journal, 14(1).
[20] Biswas, N., Santra, D., Banerjee, B., & Biswas, S. (2024). Harnessing the Power of Machine Learning for Parkinson's Disease Detection. In AIoT and Smart Sensing Technologies for Smart Devices (pp. 140-155). IGI Global.
[21] Saha, G., Banerjee, B., & Joshi, F. M. (2022). Predictive Edge Computing of SST Time-Series-Based Marine Warning System using Cloud Computing Infrastructure. In Cloud IoT (pp. 59-74). Chapman and Hall/CRC.
[22] Banerjee, B., & Saha, G. (2022). Emotion Independent Face Recognition-Based Security Protocol in IoT-Enabled Devices. In Cloud IoT (pp. 199-218). Chapman and Hall/CRC.
[23] Banerjee, B., Jani, A., & Shah, N. (2021). A genetic blockchain approach for securing smart vehicles in quantum era. In Vehicular Communications for Smart Cars (pp. 85-108). CRC Press.
[24] Banerjee, B., Jani, A., & Shah, N. (2021). Digital Image Encryption Using Double Crossover Approach for SARS-CoV-2 Infected Lungs in a Blockchain Framework. Frontiers in Blockchain, 4, 771241.
[25] Banerjee, B., Hazra, D., & Sarkar, D. (2024). IoT-Enabled Water Quality Management System for Rural Areas of Bharuch District. In Water Informatics: Challenges and Solutions Using State of Art Technologies (pp. 33-47). Singapore: Springer Nature Singapore.
[26] Patel, M. K., Uchhula, V., & Banerjee, B. Comparative Evaluation of AODV, DSDV and AOMDV based on end-to-end delay and routing overhead using Network Simulator.
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